In the oil and gas industry, seismic prospecting and other similar techniques are commonly used to aid in the search for and evaluation of subterranean hydrocarbon deposits. An exemplary prospecting operation includes three stages: data acquisition, data processing, and data interpretation. The success of the prospecting operation depends on satisfactory completion of the three stages. In an exemplary data acquisition stage, a seismic source is used to generate an acoustic signal that propagates into the earth and is at least partially reflected by subsurface seismic reflectors. The reflected signals are detected and recorded by an array of seismic receivers located at or near the surface of the earth, in an overlying body of water, or at known depths in boreholes. During an exemplary data processing stage, the recorded seismic signals, e.g., seismic amplitude response, are refined and enhanced using a variety of procedures that depend on the nature of the geologic structure being investigated and on the characteristics of the raw data. In general, the purpose of the data processing stage is to produce an image of the subsurface from the recorded seismic data for use during the data interpretation stage. The purpose of the data interpretation stage is to determine information about the subsurface geology of the earth from the processed seismic data. The results of the data interpretation stage may be used to determine the general geologic structure of a subsurface region, or to locate potential hydrocarbon reservoirs, or to guide the development of an already discovered reservoir.
To interpret, a three-dimensional (3D) data volume may be either manually interpreted or interpreted through an automated method. A “data volume” or “volume” includes one or more slices or traces (e.g. a collection of samples as a function of time (t) for one position in the earth, such as seismic traces). The collection of traces or slices forming an array are commonly referred to as “data volumes.” The data volume depicts the subsurface layering of a portion of the earth. It is the principal tool that a geophysicist uses to determine the nature of the earth's subsurface formations. The data volume can be studied either by plotting it on paper or displaying it on a computer monitor. A geophysicist can then interpret the information. When displaying the data volume along a principle direction, crosslines, inlines, time slices, or horizon slices can be made. The data volume can be mathematically processed in accordance with known techniques to make subtle features in the data more discernible. The results of these processing techniques are known as “attributes.” The images may also be compared over a period of time to follow the evolution of the subsurface formation over time. Either of these methods may use computer-aided interpretation tools to accelerate interpretation of prospecting data (e.g., seismic, controlled source electromagnetic, or other suitable data) for detecting geologic anomalies (e.g. geologic bodies of interest) or tracking boundaries of geologic objects of interest. These geologic objects include geologic horizon surfaces, fault surfaces, stratigraphic traps, and channels, for example.
Manual interpretation typically involves the manual picking or digitizing of each geologic object of interest using the data volume as a visual guide. If this is done in a computer aided interpretation system, this involves digitizing the geologic objects on cross sections/slices or volumes using a cursor, tablet or some other input device. Additional seismic attribute volumes may be used to make the final interpretation. With manual interpretation, the interpreter keeps track of 3D complexity and geologic complexity. As such, this increases the risk for incorrect interpretation of geologic features and also greatly increases the time involved to complete the interpretation.
Alternatively, automated methods for tracking geologic objects, such as horizons and faults, have existed in the industry for twenty years. However, automated methods have limitations that hinder their effectiveness for certain types of interpretation. For instance, the automated methods may not be applicable for addressing certain interpretation problems. In particular, typical automated methods require that the feature to be tracked or extended follows a consistent or similar seismic amplitude/attributes, such as peak, trough, zero crossing, within a value range. This limitation restricts the applicability of these methods, because many of the more interesting and geologically significant surfaces that need to be interpreted do not satisfy this limitation. Examples of these geologic objects include; salt/shale diapirs, channels, unconformities, and faults and other stratigraphic features. In addition, the automated systems are also limited by the data quality and the complexity of the geology. For instance, while automated methods can be more accurate than manual methods when applied to higher quality data and simple geology, these automated methods become more error prone as the data quality decreases and the complexity of the geology increases. As such, when automated results become too error prone, the amount of time required to find and correct the errors exceeds the time to manually interpret the geologic objects. Therefore, automated methods are frequently not used for large amount of interpretation tasks due to the limitations discussed above.
The present techniques, which are described below, address weaknesses of both conventional automated methods and manual interpretation processes in tracking/extending more complex boundaries of geologic objects of interests. As a result, the present techniques may be used to reduce interpretation time, provide more accurate interpretations, and detect geologic objects (i.e. anomalous geologic regions) in prospecting data volume (e.g. seismic data and derivative volumes).
Other related material may be found in at least U.S. Pat. Nos. 5,455,896; 6,480,615; 6,690,820; 6,765,570; 6,731,799; 7,068,831; 7,200,602 and 7,248,258 and Fitsum Admasu and Klaus Tonnies, “An Approach towards Automated Fault Interpretations in Seismic Data”, SimVis 2005.